How to Re-Rate Your Pre-AI SaaS Assets into AI Valuations
From 2016 through 2021, many workflow-centric SaaS companies attracted enormous amounts of capital. A large portion of the application layer (e.g. productivity, CX, operations, vertical SaaS, analytics, collaboration, devtools), was still mid-cycle in its modernization. The pandemic accelerated this shift as digital workflows became the operating system for business interactions.
Accel’s Globalscape index shows how dramatically things changed. Cloud and SaaS valuations peaked in 2021 and corrected sharply: EV/NTM revenue multiples for leading public cloud companies fell to 7.8x as of October 2025, down from the mid-teens at the peak and only modestly above the 7.1x pre-COVID average. Growth also slowed from the mid-40% range in 2023 to the low-20% range by 2025.
In short: SaaS was priced for perfection. Then macro reality arrived in 2022.
Rates rose, funding cooled, and “growth at all costs” became “get efficient fast.” Many SaaS platforms were still absorbing that shift when the AI wave raised the competitive bar again.
A new competitive bar
AI-native platforms emerged with entirely different characteristics like agentic workflows, embedded intelligence, rapid shipping cadence, and automation-first economics. PitchBook’s 2025 AI report shows AI/ML companies now represent nearly 60% of global VC deal value, up sharply from a year earlier. These companies are built for hyper-growth from day one.
The bar for what counts as “high growth” moved upward and toward AI-first architecture, decisioning, and automation.
The valuation and liquidity gap PE now faces
If you invested in SaaS companies at 2020–2021 prices, today’s environment is challenging.
1. Public markets have outperformed private equity
State Street’s PMI shows the S&P 500 outpacing private equity across 1-, 3-, 5-, and 10-year periods as of Q4 2024.
2. Distributions have fallen sharply
Bain’s 2025 report shows buyout distributions dropping from ~29% of NAV in 2014–2017 to ~11% in 2024. MSCI data shows similar patterns in the 8–9% range.
3. A massive backlog of unsold assets
Bain and PitchBook estimate roughly 29,000–30,000 unsold PE-owned portfolio companies globally, most purchased from 2017–2021 at high entry multiples.
4. Enormous dry powder
Global PE dry powder still sits around ~$4 trillion. One of the highest levels ever recorded.
The result:
Public markets offer returns and liquidity.
Private equity is delivering weaker performance, low DPI, and a large inventory of hard-to-exit assets.
The dilemma as a private equity sponsor for a 2016 - 2021 SaaS asset is obvious:
Do you take the loss?
Do you hold and hope?
Or do you reposition?
Many assets will not clear the bar. But many were not bad investments. They had good teams, embedded customers, and strong product-market fit. They simply collided with the wrong macro moment and an abrupt shift in technology cycles.
The answer: re-rate the right platforms into AI multiples
This is not about adding AI features. It is about transforming selected SaaS platforms so they behave, scale, automate, and monetize like AI-era systems.
Today’s market makes the contrast clear:
Traditional SaaS trades around 3–6x revenue
AI-enabled platforms and top-quartile cloud trade in the low- to mid-teens
The spread is structural. The market is paying for automation, agent-led workflows, stronger NRR, better unit economics, and lower cost-to-serve.
The opportunity is to identify which platforms can credibly evolve into this AI cohort, and move decisively.
The playbook breaks into three steps.
1. Identify the companies with real staying power
Start with honest segmentation. Only a subset of assets can realistically make the leap.
You’re looking for platforms that:
have stable or cash-generating cores
serve deeply embedded customers with high workflow dependence
sit on clean or clean-able domain data and entity models
have leadership teams that can execute transformation
participate in categories where AI expands TAM and usage
A simple test: Does AI make this platform more central to the customer’s workflow, or less?
Platforms that score well can be repositioned. Others should be managed for cash or prepared for continuation or secondary processes, not held indefinitely in hopes of a valuation rebound.
2. Fund targeted acquisitions that strengthen the core
For pre-AI platforms, the fastest path to relevance is focused capability acquisition, not scattershot buying.
Recent deal trends show:
strategic software M&A >$500M is accelerating
sponsors are modernizing legacy platforms with tight roll-ups
sub-$100M talent-dense AI acquisitions are becoming common
The brief should be narrow and tied to the product vision.
Look for:
small AI teams with strong product + engineering DNA
capabilities that integrate cleanly into the platform’s data model and workflow engine
agentic or predictive systems that automate meaningful work
assets that support one coherent platform strategy, not a toolkit of disconnected features
A simple lens:
AI that accelerates core workflows
AI that expands use cases inside the same buyer profile
AI that deepens vertical or geographic strength
You are not trying to outspend AI labs.
You’re buying acceleration and talent, not scale.
This is how a 3–5x SaaS asset becomes a mid-teens AI asset, and where bolt-on M&A directly drives DPI and exit readiness.
3. Back product leadership with real conviction
The biggest risk today isn’t making the wrong bet; it’s failing to make one at all.
PE investors are under pressure:
LP over-allocation, low DPI, weak distributions, and public benchmarks outperforming private markets.
The instinct is caution.
But “wait and see” leads to:
stalled product
zero AI credibility
no uplift in NRR or margins
and no multiple expansion
Teams with a real AI transformation plan need:
actual capital (not optionality)
permission to make non-incremental bets
a mandate to rebuild architecture, workflows, and GTM
sponsors willing to support smart risk-taking
You can already see this dynamic among category leaders. Microsoft, ServiceNow, Salesforce, Atlassian, HubSpot, Datadog, and several vertical SaaS winners are reorganizing product lines around agentic automation, intelligence, and cross-product workflows.
Mid-market and vertical SaaS platforms need similar alignment if they want to compete.
Where that alignment is missing, you get AI theater, decks, and pilots with no durable improvement in NRR, margins, or valuation. I see a lot of this in the pre-AI SaaS space right now.
Why this matters now
SaaS multiples have reset.
AI multiples have not.
That gap is an opening, not a constraint.
You can:
take a discounted SaaS asset valued at 3–5x
rebuild it into an AI-era platform
use targeted M&A to accelerate capability gaps
and re-rate it closer to mid-teens AI/cloud multiples
That’s how a mark-to-market headache turns into a real DPI story.
Not every company will clear the bar.
But the subset that can, and do, will reward investors who act with clarity instead of caution.
This isn’t about trying to recreate 2021.
It’s about recognizing that some SaaS platforms were strong then, and can be strong again, if they evolve fast enough.
The right questions now are:
Which assets can credibly be re-rated into the AI cohort?
And are we prepared to move decisively on those?
Next: Making the integrations actually work
In the next post, I’ll break down the post-merger integration (PMI) side of this transformation, how to align data models, integrate AI teams, merge workflows, and retain critical talent. Most AI acquisitions don’t fail because of the technology. They fail because the integration playbook wasn’t designed for AI-era architecture.
If you’re building, buying, or operating in this space, I’d love to compare notes.
You can reach me at faraaz@inorganicedge.com or on LinkedIn.
Sources
Accel – 2025 Globalscape Report
https://www.accel.com/noteworthy/globalscape
Cloud valuations, revenue growth benchmarks, and category resets since 2021.
Bain & Company – Global Private Equity Report 2025
https://www.bain.com/insights/topics/global-private-equity-report/
Distribution trends, backlog of unsold portfolio companies, and dry powder analysis.
PitchBook – Q1 2025 AI & ML Market Update
https://pitchbook.com/news/reports
Data on AI/ML VC deal value, category growth, and shifting investment share.
PitchBook – Global PE & VC Fund Performance
https://pitchbook.com/news/reports
DPI, TVPI, and long-horizon performance trends for buyout funds.
State Street – Private Market Index (PMI)
https://www.statestreet.com
Comparison of private markets vs. S&P 500 across 1-, 3-, 5-, and 10-year periods.
MSCI – Private Capital Quarterly Review
https://www.msci.com
Insights on declining buyout distribution rates and NAV dynamics.
McKinsey – 2025 Global M&A Outlook
https://www.mckinsey.com/capabilities/mergers-and-acquisitions
Trends in software M&A >$500M and capability-driven acquisitions.
Dealroom – AI & Cloud Funding Landscape 2025
https://dealroom.co
Global AI and cloud funding trends; increase in smaller talent-led acquisitions.
Bessemer – State of the Cloud 2025
https://www.bessemer.com/cloud
Analysis of AI-native growth profiles, “AI supernovas,” and benchmark ARR trajectories.
